Overview

Dataset statistics

Number of variables25
Number of observations250000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory46.0 MiB
Average record size in memory193.0 B

Variable types

Numeric19
DateTime2
Categorical4

Warnings

cumulativeAudio has constant value "19" Constant
cumulativeShock has constant value "5" Constant
validPosition has constant value "1" Constant
df_index is highly correlated with lon_wMins and 4 other fieldsHigh correlation
lon_wMins is highly correlated with df_index and 2 other fieldsHigh correlation
hdop is highly correlated with df_index and 2 other fieldsHigh correlation
satellitesUsed is highly correlated with df_index and 4 other fieldsHigh correlation
accel_mg.x is highly correlated with pitchHigh correlation
accel_mg.y is highly correlated with df_index and 4 other fieldsHigh correlation
accel_mg.z is highly correlated with df_index and 2 other fieldsHigh correlation
pitch is highly correlated with accel_mg.xHigh correlation
df_index is highly correlated with lon_wMins and 4 other fieldsHigh correlation
lon_wMins is highly correlated with df_index and 3 other fieldsHigh correlation
hdop is highly correlated with df_index and 4 other fieldsHigh correlation
satellitesUsed is highly correlated with df_index and 4 other fieldsHigh correlation
accel_mg.x is highly correlated with accel_mg.y and 1 other fieldsHigh correlation
accel_mg.y is highly correlated with df_index and 6 other fieldsHigh correlation
accel_mg.z is highly correlated with df_index and 3 other fieldsHigh correlation
pitch is highly correlated with accel_mg.x and 1 other fieldsHigh correlation
df_index is highly correlated with lon_wMins and 4 other fieldsHigh correlation
lon_wMins is highly correlated with df_index and 2 other fieldsHigh correlation
hdop is highly correlated with df_index and 3 other fieldsHigh correlation
satellitesUsed is highly correlated with df_index and 3 other fieldsHigh correlation
accel_mg.x is highly correlated with pitchHigh correlation
accel_mg.y is highly correlated with df_index and 2 other fieldsHigh correlation
accel_mg.z is highly correlated with df_indexHigh correlation
pitch is highly correlated with accel_mg.xHigh correlation
accel_mg.y is highly correlated with UnixTime and 8 other fieldsHigh correlation
UnixTime is highly correlated with accel_mg.y and 8 other fieldsHigh correlation
mag_nT.z is highly correlated with accel_mg.y and 9 other fieldsHigh correlation
gyro_dps.x is highly correlated with gyro_dps.zHigh correlation
IMUfSpeed is highly correlated with activityHigh correlation
lon_wMins is highly correlated with accel_mg.y and 10 other fieldsHigh correlation
gyro_dps.y is highly correlated with gyro_dps.zHigh correlation
df_index is highly correlated with accel_mg.y and 12 other fieldsHigh correlation
gyro_dps.z is highly correlated with gyro_dps.x and 1 other fieldsHigh correlation
lat_sMins is highly correlated with accel_mg.y and 10 other fieldsHigh correlation
accel_mg.z is highly correlated with accel_mg.y and 8 other fieldsHigh correlation
gps_unixTime is highly correlated with accel_mg.y and 11 other fieldsHigh correlation
pitch is highly correlated with mag_nT.z and 10 other fieldsHigh correlation
heading_hundredths is highly correlated with lon_wMins and 4 other fieldsHigh correlation
mag_nT.y is highly correlated with mag_nT.z and 8 other fieldsHigh correlation
velocity_cm_s is highly correlated with activityHigh correlation
satellitesUsed is highly correlated with accel_mg.y and 12 other fieldsHigh correlation
mag_nT.x is highly correlated with accel_mg.y and 12 other fieldsHigh correlation
hdop is highly correlated with UnixTime and 2 other fieldsHigh correlation
activity is highly correlated with IMUfSpeed and 7 other fieldsHigh correlation
accel_mg.x is highly correlated with pitch and 1 other fieldsHigh correlation
cumulativeAudio is highly correlated with activity and 2 other fieldsHigh correlation
activity is highly correlated with cumulativeAudio and 2 other fieldsHigh correlation
validPosition is highly correlated with cumulativeAudio and 2 other fieldsHigh correlation
cumulativeShock is highly correlated with cumulativeAudio and 2 other fieldsHigh correlation
df_index is uniformly distributed Uniform
df_index has unique values Unique
velocity_cm_s has 157587 (63.0%) zeros Zeros
gyro_dps.x has 38302 (15.3%) zeros Zeros
gyro_dps.y has 46474 (18.6%) zeros Zeros
gyro_dps.z has 71272 (28.5%) zeros Zeros
IMUspeed has 50696 (20.3%) zeros Zeros
pitch has 6563 (2.6%) zeros Zeros

Reproduction

Analysis started2021-08-15 10:00:52.116127
Analysis finished2021-08-15 10:02:51.019066
Duration1 minute and 58.9 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct250000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean278971.856
Minimum0
Maximum558399
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2021-08-15T20:02:51.111712image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile27940.95
Q1139397.75
median278853.5
Q3418360.25
95-th percentile530475.2
Maximum558399
Range558399
Interquartile range (IQR)278962.5

Descriptive statistics

Standard deviation161102.8929
Coefficient of variation (CV)0.5774879776
Kurtosis-1.198601397
Mean278971.856
Median Absolute Deviation (MAD)139482
Skewness0.002489939113
Sum6.9742964 × 1010
Variance2.59541421 × 1010
MonotonicityNot monotonic
2021-08-15T20:02:51.288959image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5263351
 
< 0.1%
2143331
 
< 0.1%
2020391
 
< 0.1%
2040861
 
< 0.1%
2102271
 
< 0.1%
5515481
 
< 0.1%
842601
 
< 0.1%
2573201
 
< 0.1%
2450261
 
< 0.1%
5313581
 
< 0.1%
Other values (249990)249990
> 99.9%
ValueCountFrequency (%)
01
< 0.1%
61
< 0.1%
81
< 0.1%
91
< 0.1%
101
< 0.1%
131
< 0.1%
151
< 0.1%
161
< 0.1%
171
< 0.1%
221
< 0.1%
ValueCountFrequency (%)
5583991
< 0.1%
5583981
< 0.1%
5583971
< 0.1%
5583961
< 0.1%
5583941
< 0.1%
5583871
< 0.1%
5583861
< 0.1%
5583841
< 0.1%
5583811
< 0.1%
5583781
< 0.1%

UnixTime
Date

HIGH CORRELATION

Distinct36
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
Minimum1970-01-01 00:00:01.532740
Maximum1970-07-29 05:36:40
2021-08-15T20:02:51.464978image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:02:51.630965image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=36)

cumulativeAudio
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
19
250000 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters500000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row19
2nd row19
3rd row19
4th row19
5th row19

Common Values

ValueCountFrequency (%)
19250000
100.0%

Length

2021-08-15T20:02:51.927153image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-15T20:02:52.015797image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
19250000
100.0%

Most occurring characters

ValueCountFrequency (%)
1250000
50.0%
9250000
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number500000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1250000
50.0%
9250000
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common500000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1250000
50.0%
9250000
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII500000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1250000
50.0%
9250000
50.0%

cumulativeShock
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
5
250000 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters250000
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row5
3rd row5
4th row5
5th row5

Common Values

ValueCountFrequency (%)
5250000
100.0%

Length

2021-08-15T20:02:52.236002image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-15T20:02:52.319855image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
5250000
100.0%

Most occurring characters

ValueCountFrequency (%)
5250000
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number250000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5250000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common250000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5250000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII250000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5250000
100.0%

lat_sMins
Real number (ℝ)

HIGH CORRELATION

Distinct232
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-27.78859648
Minimum-27.78898
Maximum-27.788231
Zeros0
Zeros (%)0.0%
Negative250000
Negative (%)100.0%
Memory size1.9 MiB
2021-08-15T20:02:52.429427image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-27.78898
5-th percentile-27.788916
Q1-27.78882
median-27.788591
Q3-27.788445
95-th percentile-27.7883
Maximum-27.788231
Range0.000749
Interquartile range (IQR)0.000375

Descriptive statistics

Standard deviation0.0001989235215
Coefficient of variation (CV)-7.158458747 × 10-6
Kurtosis-1.125086068
Mean-27.78859648
Median Absolute Deviation (MAD)0.000154
Skewness-0.09443664309
Sum-6947149.12
Variance3.957056742 × 10-8
MonotonicityNot monotonic
2021-08-15T20:02:52.644133image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-27.78849627230
 
10.9%
-27.78883614733
 
5.9%
-27.788313593
 
5.4%
-27.78886211814
 
4.7%
-27.78858211485
 
4.6%
-27.78831310327
 
4.1%
-27.7884378121
 
3.2%
-27.7883436729
 
2.7%
-27.788346423
 
2.6%
-27.7885575756
 
2.3%
Other values (222)133789
53.5%
ValueCountFrequency (%)
-27.7889824
 
< 0.1%
-27.78897921
 
< 0.1%
-27.78897717
 
< 0.1%
-27.78897122
 
< 0.1%
-27.78896547
 
< 0.1%
-27.788963279
 
0.1%
-27.78895675
 
< 0.1%
-27.788954493
0.2%
-27.788951091
0.4%
-27.78894626
 
< 0.1%
ValueCountFrequency (%)
-27.78823189
 
< 0.1%
-27.78827921
 
< 0.1%
-27.7882821513
 
0.6%
-27.78828422
 
< 0.1%
-27.788286116
 
< 0.1%
-27.788292109
 
< 0.1%
-27.78829468
 
< 0.1%
-27.788298118
 
< 0.1%
-27.788313593
5.4%
-27.788301880
 
0.4%

lon_wMins
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct189
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean152.6865837
Minimum152.685593
Maximum152.689301
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2021-08-15T20:02:52.848674image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum152.685593
5-th percentile152.685715
Q1152.685959
median152.686234
Q3152.686615
95-th percentile152.689011
Maximum152.689301
Range0.003708
Interquartile range (IQR)0.000656

Descriptive statistics

Standard deviation0.0010114717
Coefficient of variation (CV)6.624496239 × 10-6
Kurtosis0.5710734914
Mean152.6865837
Median Absolute Deviation (MAD)0.000366
Skewness1.362838914
Sum38171645.93
Variance1.023074999 × 10-6
MonotonicityNot monotonic
2021-08-15T20:02:53.037222image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
152.68598929775
 
11.9%
152.68574523481
 
9.4%
152.68629518247
 
7.3%
152.68595915874
 
6.3%
152.68655412937
 
5.2%
152.68571512417
 
5.0%
152.68644710489
 
4.2%
152.687799805
 
3.9%
152.6866155736
 
2.3%
152.6858065644
 
2.3%
Other values (179)105595
42.2%
ValueCountFrequency (%)
152.68559387
 
< 0.1%
152.68562389
 
< 0.1%
152.685684323
 
0.1%
152.68569979
 
< 0.1%
152.68571512417
5.0%
152.685732939
 
1.2%
152.68574523481
9.4%
152.685764716
 
1.9%
152.6857764439
 
1.8%
152.6857912135
 
0.9%
ValueCountFrequency (%)
152.689301164
 
0.1%
152.6892852520
1.0%
152.6892738
 
< 0.1%
152.68925546
 
< 0.1%
152.6891633388
1.4%
152.68914848
 
< 0.1%
152.68913340
 
< 0.1%
152.6891171497
0.6%
152.689102940
 
0.4%
152.689056662
 
0.3%

gps_unixTime
Date

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
Minimum1970-01-01 00:00:01.532740
Maximum1970-01-01 00:00:01.532760
2021-08-15T20:02:53.203945image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:02:53.320678image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=3)

heading_hundredths
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1410
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean163.422131
Minimum0.08
Maximum359.16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2021-08-15T20:02:53.474848image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.08
5-th percentile13.37
Q170.93
median128.28
Q3287.49
95-th percentile349.92
Maximum359.16
Range359.08
Interquartile range (IQR)216.56

Descriptive statistics

Standard deviation117.8792291
Coefficient of variation (CV)0.721317415
Kurtosis-1.367552736
Mean163.422131
Median Absolute Deviation (MAD)87.68
Skewness0.3473012519
Sum40855532.74
Variance13895.51264
MonotonicityNot monotonic
2021-08-15T20:02:53.662261image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
349.9227136
 
10.9%
78.0714097
 
5.6%
287.4912693
 
5.1%
21.8611263
 
4.5%
209.939911
 
4.0%
306.639696
 
3.9%
78.658074
 
3.2%
52.846499
 
2.6%
336.156217
 
2.5%
13.375472
 
2.2%
Other values (1400)138942
55.6%
ValueCountFrequency (%)
0.0814
 
< 0.1%
0.17111
 
< 0.1%
0.6925
 
< 0.1%
1.7426
 
< 0.1%
1.8417
 
< 0.1%
1.9323
 
< 0.1%
2.1520
 
< 0.1%
2.4117
 
< 0.1%
2.723409
1.4%
3.88164
 
0.1%
ValueCountFrequency (%)
359.1625
 
< 0.1%
359.0277
< 0.1%
358.8426
 
< 0.1%
358.6741
 
< 0.1%
358.4166
0.1%
358.2122
 
< 0.1%
357.8526
 
< 0.1%
357.5422
 
< 0.1%
357.4216
 
< 0.1%
357.1924
 
< 0.1%

velocity_cm_s
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct96
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.367204
Minimum0
Maximum711
Zeros157587
Zeros (%)63.0%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2021-08-15T20:02:53.846784image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q319
95-th percentile19
Maximum711
Range711
Interquartile range (IQR)19

Descriptive statistics

Standard deviation19.16063344
Coefficient of variation (CV)2.045501885
Kurtosis328.6471441
Mean9.367204
Median Absolute Deviation (MAD)0
Skewness10.799691
Sum2341801
Variance367.1298737
MonotonicityNot monotonic
2021-08-15T20:02:54.024410image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0157587
63.0%
1976274
30.5%
80523
 
0.2%
13497
 
0.2%
14461
 
0.2%
4428
 
0.2%
78426
 
0.2%
3409
 
0.2%
74378
 
0.2%
77377
 
0.2%
Other values (86)12640
 
5.1%
ValueCountFrequency (%)
0157587
63.0%
157
 
< 0.1%
2287
 
0.1%
3409
 
0.2%
4428
 
0.2%
5366
 
0.1%
6313
 
0.1%
7317
 
0.1%
8371
 
0.1%
9291
 
0.1%
ValueCountFrequency (%)
71145
 
< 0.1%
15820
 
< 0.1%
13847
 
< 0.1%
12221
 
< 0.1%
11320
 
< 0.1%
10319
 
< 0.1%
10223
 
< 0.1%
10145
 
< 0.1%
10020
 
< 0.1%
99147
0.1%

hdop
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5730756
Minimum0.5
Maximum4.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2021-08-15T20:02:54.189589image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile0.5
Q10.5
median0.6
Q30.6
95-th percentile0.8
Maximum4.1
Range3.6
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation0.1121693675
Coefficient of variation (CV)0.1957322342
Kurtosis104.5824615
Mean0.5730756
Median Absolute Deviation (MAD)0.1
Skewness5.944786656
Sum143268.9
Variance0.01258196701
MonotonicityNot monotonic
2021-08-15T20:02:54.315563image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0.5119273
47.7%
0.6108898
43.6%
0.77914
 
3.2%
0.87110
 
2.8%
0.93679
 
1.5%
11338
 
0.5%
1.3583
 
0.2%
1.4548
 
0.2%
1.1277
 
0.1%
1.2238
 
0.1%
Other values (5)142
 
0.1%
ValueCountFrequency (%)
0.5119273
47.7%
0.6108898
43.6%
0.77914
 
3.2%
0.87110
 
2.8%
0.93679
 
1.5%
11338
 
0.5%
1.1277
 
0.1%
1.2238
 
0.1%
1.3583
 
0.2%
1.4548
 
0.2%
ValueCountFrequency (%)
4.122
 
< 0.1%
224
 
< 0.1%
1.924
 
< 0.1%
1.723
 
< 0.1%
1.649
 
< 0.1%
1.4548
0.2%
1.3583
0.2%
1.2238
 
0.1%
1.1277
 
0.1%
11338
0.5%

satellitesUsed
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.186456
Minimum4
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2021-08-15T20:02:54.495439image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile11
Q118
median19
Q319
95-th percentile21
Maximum22
Range18
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.556341976
Coefficient of variation (CV)0.1405629539
Kurtosis5.33823281
Mean18.186456
Median Absolute Deviation (MAD)1
Skewness-2.119214367
Sum4546614
Variance6.5348843
MonotonicityNot monotonic
2021-08-15T20:02:54.689395image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
1980467
32.2%
1862681
25.1%
2037730
15.1%
1719323
 
7.7%
2110874
 
4.3%
229516
 
3.8%
169033
 
3.6%
105853
 
2.3%
93145
 
1.3%
112807
 
1.1%
Other values (9)8571
 
3.4%
ValueCountFrequency (%)
422
 
< 0.1%
548
 
< 0.1%
645
 
< 0.1%
7237
 
0.1%
81835
 
0.7%
93145
1.3%
105853
2.3%
112807
1.1%
12534
 
0.2%
131532
 
0.6%
ValueCountFrequency (%)
229516
 
3.8%
2110874
 
4.3%
2037730
15.1%
1980467
32.2%
1862681
25.1%
1719323
 
7.7%
169033
 
3.6%
152733
 
1.1%
141585
 
0.6%
131532
 
0.6%

validPosition
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
1
250000 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters250000
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1250000
100.0%

Length

2021-08-15T20:02:54.985307image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-15T20:02:55.067179image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1250000
100.0%

Most occurring characters

ValueCountFrequency (%)
1250000
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number250000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1250000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common250000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1250000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII250000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1250000
100.0%

accel_mg.x
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1844
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean406.134472
Minimum-2000
Maximum1999
Zeros174
Zeros (%)0.1%
Negative52265
Negative (%)20.9%
Memory size1.9 MiB
2021-08-15T20:02:55.169031image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-2000
5-th percentile-267
Q1115
median511
Q3680
95-th percentile825
Maximum1999
Range3999
Interquartile range (IQR)565

Descriptive statistics

Standard deviation356.0776627
Coefficient of variation (CV)0.8767481888
Kurtosis-0.5814850257
Mean406.134472
Median Absolute Deviation (MAD)199
Skewness-0.7518382126
Sum101533618
Variance126791.3019
MonotonicityNot monotonic
2021-08-15T20:02:55.363971image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5201043
 
0.4%
5181035
 
0.4%
519983
 
0.4%
515934
 
0.4%
516924
 
0.4%
521918
 
0.4%
517882
 
0.4%
513802
 
0.3%
500797
 
0.3%
502785
 
0.3%
Other values (1834)240897
96.4%
ValueCountFrequency (%)
-20003
< 0.1%
-15031
 
< 0.1%
-14941
 
< 0.1%
-13371
 
< 0.1%
-12021
 
< 0.1%
-10211
 
< 0.1%
-10121
 
< 0.1%
-9791
 
< 0.1%
-9381
 
< 0.1%
-9121
 
< 0.1%
ValueCountFrequency (%)
19992
< 0.1%
18581
< 0.1%
18181
< 0.1%
17451
< 0.1%
17341
< 0.1%
16551
< 0.1%
16351
< 0.1%
16251
< 0.1%
15311
< 0.1%
14751
< 0.1%

accel_mg.y
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1901
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean449.96624
Minimum-2000
Maximum1999
Zeros188
Zeros (%)0.1%
Negative13033
Negative (%)5.2%
Memory size1.9 MiB
2021-08-15T20:02:55.627113image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-2000
5-th percentile-3
Q1136
median330
Q3836
95-th percentile994
Maximum1999
Range3999
Interquartile range (IQR)700

Descriptive statistics

Standard deviation365.7456432
Coefficient of variation (CV)0.8128290763
Kurtosis-1.356203958
Mean449.96624
Median Absolute Deviation (MAD)262
Skewness0.3063451286
Sum112491560
Variance133769.8755
MonotonicityNot monotonic
2021-08-15T20:02:55.827372image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7741188
 
0.5%
7751129
 
0.5%
7761039
 
0.4%
7731027
 
0.4%
777965
 
0.4%
778956
 
0.4%
781814
 
0.3%
780807
 
0.3%
779799
 
0.3%
782769
 
0.3%
Other values (1891)240507
96.2%
ValueCountFrequency (%)
-20001
< 0.1%
-15691
< 0.1%
-12671
< 0.1%
-9631
< 0.1%
-8581
< 0.1%
-8031
< 0.1%
-7851
< 0.1%
-7411
< 0.1%
-7351
< 0.1%
-7051
< 0.1%
ValueCountFrequency (%)
19998
< 0.1%
19021
 
< 0.1%
18801
 
< 0.1%
18611
 
< 0.1%
18181
 
< 0.1%
17751
 
< 0.1%
17321
 
< 0.1%
17191
 
< 0.1%
17021
 
< 0.1%
16291
 
< 0.1%

accel_mg.z
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2002
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean515.126848
Minimum-2000
Maximum1999
Zeros177
Zeros (%)0.1%
Negative33809
Negative (%)13.5%
Memory size1.9 MiB
2021-08-15T20:02:56.042468image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-2000
5-th percentile-168
Q1181
median650
Q3802
95-th percentile967
Maximum1999
Range3999
Interquartile range (IQR)621

Descriptive statistics

Standard deviation378.0408402
Coefficient of variation (CV)0.7338791245
Kurtosis-0.7831623678
Mean515.126848
Median Absolute Deviation (MAD)239
Skewness-0.6078247773
Sum128781712
Variance142914.8769
MonotonicityNot monotonic
2021-08-15T20:02:56.233144image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
747822
 
0.3%
408783
 
0.3%
750762
 
0.3%
411758
 
0.3%
746755
 
0.3%
744752
 
0.3%
745749
 
0.3%
409741
 
0.3%
748733
 
0.3%
752682
 
0.3%
Other values (1992)242463
97.0%
ValueCountFrequency (%)
-20001
< 0.1%
-10361
< 0.1%
-10281
< 0.1%
-10261
< 0.1%
-9331
< 0.1%
-8381
< 0.1%
-7722
< 0.1%
-7441
< 0.1%
-7061
< 0.1%
-6911
< 0.1%
ValueCountFrequency (%)
19992
< 0.1%
19441
< 0.1%
18331
< 0.1%
17741
< 0.1%
17261
< 0.1%
16941
< 0.1%
16731
< 0.1%
16541
< 0.1%
15991
< 0.1%
15981
< 0.1%

gyro_dps.x
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct69
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.754036
Minimum-117
Maximum47
Zeros38302
Zeros (%)15.3%
Negative157128
Negative (%)62.9%
Memory size1.9 MiB
2021-08-15T20:02:56.460545image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-117
5-th percentile-5
Q1-2
median-1
Q30
95-th percentile3
Maximum47
Range164
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.467891828
Coefficient of variation (CV)-3.272909818
Kurtosis51.0806651
Mean-0.754036
Median Absolute Deviation (MAD)1
Skewness-0.8518667098
Sum-188509
Variance6.090490073
MonotonicityNot monotonic
2021-08-15T20:02:56.647545image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-191300
36.5%
038302
15.3%
-225799
 
10.3%
122605
 
9.0%
-316861
 
6.7%
213951
 
5.6%
-49845
 
3.9%
38065
 
3.2%
-55807
 
2.3%
44238
 
1.7%
Other values (59)13227
 
5.3%
ValueCountFrequency (%)
-1171
< 0.1%
-1111
< 0.1%
-861
< 0.1%
-571
< 0.1%
-491
< 0.1%
-411
< 0.1%
-322
< 0.1%
-301
< 0.1%
-292
< 0.1%
-282
< 0.1%
ValueCountFrequency (%)
471
 
< 0.1%
362
< 0.1%
331
 
< 0.1%
312
< 0.1%
302
< 0.1%
291
 
< 0.1%
284
< 0.1%
261
 
< 0.1%
251
 
< 0.1%
243
< 0.1%

gyro_dps.y
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct68
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.641236
Minimum-111
Maximum62
Zeros46474
Zeros (%)18.6%
Negative150927
Negative (%)60.4%
Memory size1.9 MiB
2021-08-15T20:02:56.855327image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-111
5-th percentile-4
Q1-1
median-1
Q30
95-th percentile3
Maximum62
Range173
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.503496248
Coefficient of variation (CV)-3.904172953
Kurtosis28.23717791
Mean-0.641236
Median Absolute Deviation (MAD)1
Skewness-0.1963587785
Sum-160309
Variance6.267493462
MonotonicityNot monotonic
2021-08-15T20:02:57.030385image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-191432
36.6%
046474
18.6%
-227625
 
11.1%
121998
 
8.8%
-314446
 
5.8%
212367
 
4.9%
37119
 
2.8%
-46884
 
2.8%
44123
 
1.6%
-53678
 
1.5%
Other values (58)13854
 
5.5%
ValueCountFrequency (%)
-1111
 
< 0.1%
-361
 
< 0.1%
-322
 
< 0.1%
-271
 
< 0.1%
-265
< 0.1%
-252
 
< 0.1%
-245
< 0.1%
-236
< 0.1%
-228
< 0.1%
-217
< 0.1%
ValueCountFrequency (%)
621
< 0.1%
481
< 0.1%
451
< 0.1%
431
< 0.1%
411
< 0.1%
391
< 0.1%
382
< 0.1%
331
< 0.1%
321
< 0.1%
311
< 0.1%

gyro_dps.z
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct45
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.51358
Minimum-57
Maximum34
Zeros71272
Zeros (%)28.5%
Negative125069
Negative (%)50.0%
Memory size1.9 MiB
2021-08-15T20:02:57.209612image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-57
5-th percentile-4
Q1-1
median-1
Q30
95-th percentile3
Maximum34
Range91
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.110171888
Coefficient of variation (CV)-4.108750122
Kurtosis7.433346164
Mean-0.51358
Median Absolute Deviation (MAD)1
Skewness-0.15958098
Sum-128395
Variance4.452825395
MonotonicityNot monotonic
2021-08-15T20:02:57.373278image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
-171742
28.7%
071272
28.5%
124913
 
10.0%
-223970
 
9.6%
213307
 
5.3%
-312882
 
5.2%
-47220
 
2.9%
37063
 
2.8%
-54179
 
1.7%
43772
 
1.5%
Other values (35)9680
 
3.9%
ValueCountFrequency (%)
-571
 
< 0.1%
-311
 
< 0.1%
-302
 
< 0.1%
-221
 
< 0.1%
-183
 
< 0.1%
-177
 
< 0.1%
-167
 
< 0.1%
-1511
 
< 0.1%
-1419
< 0.1%
-1332
< 0.1%
ValueCountFrequency (%)
341
 
< 0.1%
271
 
< 0.1%
261
 
< 0.1%
241
 
< 0.1%
221
 
< 0.1%
201
 
< 0.1%
171
 
< 0.1%
154
 
< 0.1%
1412
< 0.1%
1319
< 0.1%

mag_nT.x
Real number (ℝ)

HIGH CORRELATION

Distinct515
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-4942.042488
Minimum-32686
Maximum32700
Zeros978
Zeros (%)0.4%
Negative149142
Negative (%)59.7%
Memory size1.9 MiB
2021-08-15T20:02:57.550753image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-32686
5-th percentile-30886
Q1-19636
median-3736
Q35100
95-th percentile25350
Maximum32700
Range65386
Interquartile range (IQR)24736

Descriptive statistics

Standard deviation16618.32905
Coefficient of variation (CV)-3.362643906
Kurtosis-0.6779381229
Mean-4942.042488
Median Absolute Deviation (MAD)11386
Skewness0.2056978826
Sum-1235510622
Variance276168860.5
MonotonicityNot monotonic
2021-08-15T20:02:57.735194image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25501333
 
0.5%
-314861297
 
0.5%
40501295
 
0.5%
30001278
 
0.5%
22501277
 
0.5%
-316361261
 
0.5%
19501256
 
0.5%
13501220
 
0.5%
28501219
 
0.5%
36001212
 
0.5%
Other values (505)237352
94.9%
ValueCountFrequency (%)
-32686518
 
0.2%
-32536626
0.3%
-32386735
0.3%
-322361068
0.4%
-32086999
0.4%
-319361094
0.4%
-317861012
0.4%
-316361261
0.5%
-314861297
0.5%
-313361177
0.5%
ValueCountFrequency (%)
32700458
0.2%
32550317
0.1%
32400309
0.1%
32250225
0.1%
32100181
 
0.1%
31950226
0.1%
31800280
0.1%
31650343
0.1%
31500276
0.1%
31350289
0.1%

mag_nT.y
Real number (ℝ)

HIGH CORRELATION

Distinct541
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3467.107816
Minimum-32700
Maximum32700
Zeros365
Zeros (%)0.1%
Negative88461
Negative (%)35.4%
Memory size1.9 MiB
2021-08-15T20:02:57.922741image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-32700
5-th percentile-31636
Q1-17700
median8850
Q321450
95-th percentile30450
Maximum32700
Range65400
Interquartile range (IQR)39150

Descriptive statistics

Standard deviation21294.53278
Coefficient of variation (CV)6.141872105
Kurtosis-1.202809038
Mean3467.107816
Median Absolute Deviation (MAD)15000
Skewness-0.4731454485
Sum866776954
Variance453457126.4
MonotonicityNot monotonic
2021-08-15T20:02:58.098575image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-322361891
 
0.8%
-319361831
 
0.7%
-320861749
 
0.7%
-316361727
 
0.7%
-323861701
 
0.7%
-317861680
 
0.7%
-325361633
 
0.7%
-314861612
 
0.6%
-313361575
 
0.6%
-310361563
 
0.6%
Other values (531)233038
93.2%
ValueCountFrequency (%)
-327008
 
< 0.1%
-326861400
0.6%
-3255018
 
< 0.1%
-325361633
0.7%
-3240020
 
< 0.1%
-323861701
0.7%
-322508
 
< 0.1%
-322361891
0.8%
-3210010
 
< 0.1%
-320861749
0.7%
ValueCountFrequency (%)
327001436
0.6%
326865
 
< 0.1%
325501124
0.4%
3253615
 
< 0.1%
324001028
0.4%
323864
 
< 0.1%
32250932
0.4%
32100794
0.3%
320866
 
< 0.1%
31950769
0.3%

mag_nT.z
Real number (ℝ)

HIGH CORRELATION

Distinct588
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4200.009112
Minimum-32700
Maximum32686
Zeros277
Zeros (%)0.1%
Negative94909
Negative (%)38.0%
Memory size1.9 MiB
2021-08-15T20:02:58.284279image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-32700
5-th percentile-29850
Q1-22500
median14086
Q323386
95-th percentile30436
Maximum32686
Range65386
Interquartile range (IQR)45886

Descriptive statistics

Standard deviation22168.02461
Coefficient of variation (CV)5.278089648
Kurtosis-1.48028792
Mean4200.009112
Median Absolute Deviation (MAD)13936
Skewness-0.3993458561
Sum1050002278
Variance491421315.3
MonotonicityNot monotonic
2021-08-15T20:02:58.457357image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-234001724
 
0.7%
-231001663
 
0.7%
-235501651
 
0.7%
-232501535
 
0.6%
-237001511
 
0.6%
-229501477
 
0.6%
-238501379
 
0.6%
-259501355
 
0.5%
-228001273
 
0.5%
-240001269
 
0.5%
Other values (578)235163
94.1%
ValueCountFrequency (%)
-32700616
0.2%
-32550725
0.3%
-32400713
0.3%
-32250696
0.3%
-32100732
0.3%
-31950704
0.3%
-31800737
0.3%
-31650639
0.3%
-31500644
0.3%
-31350667
0.3%
ValueCountFrequency (%)
32686684
0.3%
32536703
0.3%
32386888
0.4%
32236627
0.3%
32086702
0.3%
31936756
0.3%
31786770
0.3%
31636805
0.3%
31486862
0.3%
31336953
0.4%

IMUspeed
Real number (ℝ≥0)

ZEROS

Distinct191
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.317884
Minimum0
Maximum403
Zeros50696
Zeros (%)20.3%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2021-08-15T20:02:58.670842image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median11
Q329
95-th percentile74
Maximum403
Range403
Interquartile range (IQR)25

Descriptive statistics

Standard deviation27.33662742
Coefficient of variation (CV)1.28233306
Kurtosis16.63178871
Mean21.317884
Median Absolute Deviation (MAD)11
Skewness3.019345331
Sum5329471
Variance747.2911989
MonotonicityNot monotonic
2021-08-15T20:02:58.874838image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
050696
20.3%
1018661
 
7.5%
916169
 
6.5%
1112479
 
5.0%
87335
 
2.9%
126228
 
2.5%
75593
 
2.2%
134980
 
2.0%
144280
 
1.7%
164129
 
1.7%
Other values (181)119450
47.8%
ValueCountFrequency (%)
050696
20.3%
12817
 
1.1%
22976
 
1.2%
33156
 
1.3%
42865
 
1.1%
53882
 
1.6%
63422
 
1.4%
75593
 
2.2%
87335
 
2.9%
916169
 
6.5%
ValueCountFrequency (%)
40324
< 0.1%
32124
< 0.1%
31812
 
< 0.1%
31420
< 0.1%
29625
< 0.1%
26721
< 0.1%
25922
< 0.1%
25821
< 0.1%
25630
< 0.1%
24230
< 0.1%

IMUfSpeed
Real number (ℝ≥0)

HIGH CORRELATION

Distinct131
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.9404
Minimum0
Maximum188
Zeros239
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2021-08-15T20:02:59.069871image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8
Q110
median17
Q327
95-th percentile48
Maximum188
Range188
Interquartile range (IQR)17

Descriptive statistics

Standard deviation15.48611918
Coefficient of variation (CV)0.7395331119
Kurtosis14.20051551
Mean20.9404
Median Absolute Deviation (MAD)7
Skewness2.754587708
Sum5235100
Variance239.8198871
MonotonicityNot monotonic
2021-08-15T20:02:59.818241image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1031374
 
12.5%
926235
 
10.5%
811905
 
4.8%
1110071
 
4.0%
127351
 
2.9%
227040
 
2.8%
166988
 
2.8%
186920
 
2.8%
176867
 
2.7%
156604
 
2.6%
Other values (121)128645
51.5%
ValueCountFrequency (%)
0239
 
0.1%
1385
 
0.2%
2527
 
0.2%
3662
 
0.3%
41100
 
0.4%
51550
 
0.6%
61978
 
0.8%
73555
 
1.4%
811905
4.8%
926235
10.5%
ValueCountFrequency (%)
18820
< 0.1%
18321
< 0.1%
17921
< 0.1%
17821
< 0.1%
17422
< 0.1%
17020
< 0.1%
15723
< 0.1%
15421
< 0.1%
15045
< 0.1%
14328
< 0.1%

pitch
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct108
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-24.970204
Minimum-72
Maximum44
Zeros6563
Zeros (%)2.6%
Negative195149
Negative (%)78.1%
Memory size1.9 MiB
2021-08-15T20:03:00.017051image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-72
5-th percentile-52
Q1-42
median-30
Q3-6
95-th percentile15
Maximum44
Range116
Interquartile range (IQR)36

Descriptive statistics

Standard deviation21.741537
Coefficient of variation (CV)-0.870699214
Kurtosis-0.7541302628
Mean-24.970204
Median Absolute Deviation (MAD)14
Skewness0.5897796903
Sum-6242551
Variance472.694431
MonotonicityNot monotonic
2021-08-15T20:03:00.199507image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-3012587
 
5.0%
-2910374
 
4.1%
-287860
 
3.1%
06563
 
2.6%
-456137
 
2.5%
-435511
 
2.2%
-445499
 
2.2%
-425465
 
2.2%
-415219
 
2.1%
-475216
 
2.1%
Other values (98)179569
71.8%
ValueCountFrequency (%)
-7289
< 0.1%
-7146
 
< 0.1%
-7042
 
< 0.1%
-6922
 
< 0.1%
-6840
 
< 0.1%
-67135
0.1%
-6635
 
< 0.1%
-6576
 
< 0.1%
-64215
0.1%
-63186
0.1%
ValueCountFrequency (%)
4425
 
< 0.1%
3417
 
< 0.1%
3325
 
< 0.1%
3220
 
< 0.1%
3116
 
< 0.1%
3026
 
< 0.1%
29131
0.1%
28132
0.1%
27213
0.1%
26147
0.1%

activity
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size244.5 KiB
1
92694 
2
80034 
0
56987 
3
20285 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters250000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row0
3rd row1
4th row2
5th row3

Common Values

ValueCountFrequency (%)
192694
37.1%
280034
32.0%
056987
22.8%
320285
 
8.1%

Length

2021-08-15T20:03:00.540186image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-15T20:03:00.637549image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
192694
37.1%
280034
32.0%
056987
22.8%
320285
 
8.1%

Most occurring characters

ValueCountFrequency (%)
192694
37.1%
280034
32.0%
056987
22.8%
320285
 
8.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number250000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
192694
37.1%
280034
32.0%
056987
22.8%
320285
 
8.1%

Most occurring scripts

ValueCountFrequency (%)
Common250000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
192694
37.1%
280034
32.0%
056987
22.8%
320285
 
8.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII250000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
192694
37.1%
280034
32.0%
056987
22.8%
320285
 
8.1%

Interactions

2021-08-15T20:01:27.523471image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:27.738050image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:27.957164image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:28.160564image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:28.363384image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:28.566243image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:28.767786image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:28.971985image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:29.173588image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:29.373050image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:29.575526image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:29.772942image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:29.962363image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:30.146119image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:30.341296image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:30.536719image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:30.730810image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:30.929118image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:31.122156image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:31.311789image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:31.527272image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:31.760055image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:31.990173image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:32.212006image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:32.433427image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:32.778283image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:33.068983image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:33.302841image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:33.547804image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:33.779763image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:33.998897image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:34.232691image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:34.474520image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:34.735057image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:34.991501image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:35.221183image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:35.461711image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:35.717977image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:35.958210image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:36.175061image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:36.430036image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:36.677098image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:36.904554image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:37.148856image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:37.392035image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:37.639928image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:37.881678image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:38.120518image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:38.353813image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:38.595577image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:38.811605image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:39.027280image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:39.263939image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:39.488880image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:39.727921image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:40.171551image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:40.399494image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:40.624577image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:40.836274image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:41.110911image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:41.331475image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:41.554653image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:41.761666image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:41.967967image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:42.177149image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:42.388841image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:42.610746image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:42.824940image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:43.033377image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:43.241277image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:43.446489image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:43.652307image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:43.873784image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:44.088933image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:44.306451image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:44.523053image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:44.724936image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:44.926879image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:45.164606image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:45.376987image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:45.660319image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:45.975941image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:46.203516image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:46.455878image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:46.677747image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:46.893705image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:47.111093image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:47.313109image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:47.530511image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:47.727893image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:47.946640image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:48.156460image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:48.551101image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:48.769946image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:48.978939image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:49.179185image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:49.380501image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:49.603178image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:49.815982image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:50.019338image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:50.226574image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:50.432308image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:50.648672image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:50.865261image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:51.082396image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:51.291186image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:51.498864image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:51.705545image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:51.898159image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:52.105232image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:52.324595image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:52.528861image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:52.748340image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:52.960737image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:53.164163image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:53.375764image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:53.621241image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:53.857252image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:54.084840image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:54.298064image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:54.523038image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:54.738587image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:54.968064image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:55.190794image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:55.413525image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:55.623372image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:55.822629image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:56.018047image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:56.222242image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:56.426225image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:56.634966image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:56.844830image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:57.051117image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:57.246616image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:01:57.454103image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-08-15T20:02:42.237058image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:02:42.454341image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:02:42.703536image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:02:42.956374image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:02:43.158332image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:02:43.362923image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:02:43.564387image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:02:43.752383image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:02:43.959003image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:02:44.158571image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:02:44.354811image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:02:44.959513image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:02:45.150042image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:02:45.349755image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:02:45.547636image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:02:45.839877image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:02:46.134148image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:02:46.343917image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:02:46.626713image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:02:46.822161image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:02:47.017392image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:02:47.209031image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:02:47.406233image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:02:47.603190image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-15T20:02:47.795250image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-08-15T20:03:00.789960image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-08-15T20:03:01.166803image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-08-15T20:03:01.545075image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-08-15T20:03:01.927361image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-08-15T20:03:02.229060image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-08-15T20:02:48.172173image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-08-15T20:02:49.410887image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexUnixTimecumulativeAudiocumulativeShocklat_sMinslon_wMinsgps_unixTimeheading_hundredthsvelocity_cm_shdopsatellitesUsedvalidPositionaccel_mg.xaccel_mg.yaccel_mg.zgyro_dps.xgyro_dps.ygyro_dps.zmag_nT.xmag_nT.ymag_nT.zIMUspeedIMUfSpeedpitchactivity
03880871970-01-01 00:00:01.532760195-27.788300152.6862951970-01-01 00:00:01.532760287.4900.51917166103263-8-22936-12000259365030-83
1293971970-01-01 00:00:01.532750195-27.788557152.6860201970-01-01 00:00:01.53275034.4800.813120985-247-10-1-7336-2010017400169-20
22370581970-01-01 00:00:01.532750195-27.788862152.6857151970-01-01 00:00:01.53275021.8600.6181521458736-1-1024450-30586-2535099-311
35160831970-01-01 00:00:01.532760195-27.788660152.6886751970-01-01 00:00:01.53276074.2300.52116942397031-201140013800220362025-392
41376251970-01-01 00:00:01.532750195-27.788780152.6858981970-01-01 00:00:01.532750211.58840.6181101871-276-34-4-94361770099001660163
5669831970-01-01 00:00:01.532750195-27.788496152.6859891970-01-01 00:00:01.532750349.9200.6181499945133-423-29867650103502537-242
63810021970-01-01 00:00:01.532760195-27.788300152.6862951970-01-01 00:00:01.532760287.4900.5191768151684-10132700-26236-177002012-441
72330201970-01-01 00:00:01.532750195-27.788862152.6857151970-01-01 00:00:01.53275021.8600.6181508456745-1-1-123100-28936-25200109-301
8621501970-01-01 00:00:01.532750195-27.788496152.6859891970-01-01 00:00:01.532750349.92190.6171489987122-31-1-65861305012750013-371
93574121970-01-01 00:00:01.532760195-27.788313152.6864471970-01-01 00:00:01.532760209.9300.5191675173690-3-10-230862655029986912-511

Last rows

df_indexUnixTimecumulativeAudiocumulativeShocklat_sMinslon_wMinsgps_unixTimeheading_hundredthsvelocity_cm_shdopsatellitesUsedvalidPositionaccel_mg.xaccel_mg.yaccel_mg.zgyro_dps.xgyro_dps.ygyro_dps.zmag_nT.xmag_nT.ymag_nT.zIMUspeedIMUfSpeedpitchactivity
249990279071970-01-01 00:00:01.532750195-27.788574152.6859591970-01-01 00:00:01.53275028.0700.6181-44993-214-100-9736-210001590011950
249991493151970-01-01 00:00:01.532750195-27.788557152.6859591970-01-01 00:00:01.53275013.3700.6181-161025-1271-61-11536-120024300131140
2499921262541970-01-01 00:00:01.532750195-27.788437152.6859591970-01-01 00:00:01.53275078.65190.6181423959152-1-1-1633621600-267004916-241
2499932682061970-01-01 00:00:01.532750195-27.788927152.6860351970-01-01 00:00:01.532750158.6300.6191740100619002-45029250-304502021-522
2499943008061970-01-01 00:00:01.532750195-27.788597152.6866151970-01-01 00:00:01.53275056.86190.619172876633-1-11500017100157367826-292
2499953376411970-01-01 00:00:01.532760195-27.788313152.6864471970-01-01 00:00:01.532760209.93190.5181773-716430-1028350-23686-121501813-481
2499963852841970-01-01 00:00:01.532760195-27.788300152.6862951970-01-01 00:00:01.532760287.49190.5191681260759-1-30-2548618150218865025-432
2499973128441970-01-01 00:00:01.532750195-27.788445152.6866151970-01-01 00:00:01.5327502.7200.6191759184629-2-1-1-316362400018436022-542
2499983138041970-01-01 00:00:01.532750195-27.788445152.6866151970-01-01 00:00:01.5327502.72190.619138732996207-3-2473622500229362526-462
2499992971531970-01-01 00:00:01.532750195-27.788651152.6866001970-01-01 00:00:01.53275015.5300.6191861435442-2-4-199002430020536015-501